Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems

YesThe exponential growth in the volume of digital image databases is making it increasingly difficult to retrieve relevant information from them. Efficient retrieval systems require distinctive features extracted from visually rich contents, represented semantically in a human perception-oriented manner. This paper presents an efficient framework to model image contents as an undirected attributed relational graph, exploiting color, texture, layout, and saliency information. The proposed method encodes salient features into this rich representative model without requiring any segmentation or clustering procedures, reducing the computational complexity. In addition, an efficient graph-matching procedure implemented on specialized hardware makes it more suitable for real-time retrieval applications. The proposed framework has been tested on three publicly available datasets, and the results prove its superiority in terms of both effectiveness and efficiency in comparison with other state-of-the-art schemes.Supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904)

UAV based wilt detection system via convolutional neural networks

YesThe significant role of plants can be observed through the dependency of animals and humans on them. Oxygen, materials, food and the beauty of the world are contributed by plants. Climate change, the decrease in pollinators, and plant diseases are causing a significant decline in both quality and coverage ratio of the plants and crops on a global scale. In developed countries, above 80 percent of rural production is produced by sharecropping. However, due to widespread diseases in plants, yields are reported to have declined by more than a half. These diseases are identified and diagnosed by the agricultural and forestry department. Manual inspection on a large area of fields requires a huge amount of time and effort, thereby reduces the effectiveness significantly. To counter this problem, we propose an automatic disease detection and classification method in radish fields by using a camera attached to an unmanned aerial vehicle (UAV) to capture high quality images from the fields and analyze them by extracting both color and texture features, then we used K-means clustering to filter radish regions and feeds them into a fine-tuned GoogleNet to detect Fusarium wilt of radish efficiently at early stage and allow the authorities to take timely action which ensures the food safety for current and future generations.Supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries(IPET) through Agri-Bio Industry Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs(MAFRA) (316033-04-2-338 SB030)

Efficient deep CNN-based fire detection and localization in video surveillance applications

Convolutional neural networks (CNNs) have yielded state-of-the-art performance in image classification and other computer vision tasks. Their application in fire detection systems will substantially improve detection accuracy, which will eventually minimize fire disasters and reduce the ecological and social ramifications. However, the major concern with CNN-based fire detection systems is their implementation in real-world surveillance networks, due to their high memory and computational requirements for inference. In this paper, we propose an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire. It uses smaller convolutional kernels and contains no dense, fully connected layers, which helps keep the computational requirements to a minimum. Despite its low computational needs, the experimental results demonstrate that our proposed solution achieves accuracies that are comparable to other, more complex models, mainly due to its increased depth. Moreover, this paper shows how a tradeoff can be reached between fire detection accuracy and efficiency, by considering the specific characteristics of the problem of interest and the variety of fire data

Efficient Deep CNN-Based Fire Detection and Localisation in Video Surveillance Applications

Convolutional neural networks (CNN) have yielded state-of-the-art performance in image classification and other computer vision tasks. Their application in fire detection systems will substantially improve detection accuracy, which will eventually minimize fire disasters and reduce the ecological and social ramifications. However, the major concern with CNN-based fire detection systems is their implementation in real-world surveillance networks, due to their high memory and computational requirements for inference. In this work, we propose an energy-friendly and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire. It uses smaller convolutional kernels and contains no dense, fully connected layers, which helps keep the computational requirements to a minimum. Despite its low computational needs, the experimental results demonstrate that our proposed solution achieves accuracies that are comparable to other, more complex models, mainly due to its increased depth. Moreover, the paper shows how a trade-off can be reached between fire detection accuracy and efficiency, by considering the specific characteristics of the problem of interest and the variety of fire data

Efficient and Robust Video Steganography Algorithms for Secure Data Communication

Over the last two decades, the science of secretly embedding and communicating data has gained tremendous significance due to the technological advancement in communication and digital content. Steganography is the art of concealing secret data in a particular interactive media transporter such as text, audio, image, and video data in order to build a covert communication between authorized parties. Nowadays, video steganography techniques are important in many video-sharing and social networking applications such as Livestreaming, YouTube, Twitter, and Facebook because of noteworthy developments in advanced video over the Internet. The performance of any steganography method, ultimately, relies on the imperceptibility, hiding capacity, and robustness against attacks. Although many video steganography methods exist, several of them lack the preprocessing stages. In addition, less security, low embedding capacity, less imperceptibility, and less robustness against attacks are other issues that affect these algorithms. This dissertation investigates and analyzes cutting edge video steganography techniques in both compressed and raw domains. Moreover, it provides solutions for the aforementioned problems by proposing new and effective methods for digital video steganography. The key objectives of this research are to develop: 1) a highly secure video steganography algorithm based on error correcting codes (ECC); 2) an increased payload video steganography algorithm in the discrete wavelet domain based on ECC; 3) a novel video steganography algorithm based on Kanade-Lucas-Tomasi (KLT) tracking and ECC; 4) a robust video steganography algorithm in the wavelet domain based on KLT tracking and ECC; 5) a new video steganography algorithm based on the multiple object tracking (MOT) and ECC; and 6) a robust and secure video steganography algorithm in the discrete wavelet and discrete cosine transformations based on MOT and ECC. The experimental results from our research demonstrate that our proposed algorithms achieve higher embedding capacity as well as better imperceptibility of stego videos. Furthermore, the preprocessing stages increase the security and robustness of the proposed algorithms against attacks when compared to state-of-the-art steganographic methods